1. Field of the Invention
The present invention relates to the field of memory systems and machine intelligence. In particular the present invention relates to machine learning
2. Description of Related Art
Recognizing objects despite different scalings, rotations and translations are tasks that humans perform readily without conscious effort. However, this remains a very difficult problem for computer vision algorithms. Likewise, the human brain is very adept at recognizing and/or predicting sequences of sounds, such as for example melodies and speech, based on prior learned experience. Somato-sensory inputs to the human brain are also handled quickly and efficiently by recognizing invariant patterns experienced by nerve endings in the skin. The current state of the art in machine intelligence cannot even begin to duplicate these abilities.
One approach to implementing invariant pattern matching for visual processing involves storing a prototype of an object in a machine memory and then attempting to match it with an incoming pattern using a variety of transformations. The number of operations required for such a match would in general be exponential in the number of object categories and transformations. The number of operations could be reduced by storing a large number of prototypes for each category and then using a distance metric to categorize objects based on the distance to a nearest prototype. However, its well known that methods based on simple distance metrics do not generalize well. Deformable templates may overcome some of the problems of patterns for the same pattern of motion. Nonetheless, even simple recognition of visually perceived objects is beyond the current state of the art in artificial intelligence, even using the fastest computer processors available.
Rigid objects may be thought of as the underlying causes for persistent visual patterns. Thus learning persistent spatio-temporal visual patterns corresponds to learning rigid objects. In a similar manner, learning of persistent audible sound patterns corresponds to learning about the causes of these sounds and learning of other perceived sensory patterns corresponds to learning about the invariant underlying causes of the perceptions. Associating these patterns with the cause corresponds to invariant pattern recognition. Current technologies do not provide viable methods or systems for machines to accomplish these learning tasks.